论文标题
元学习的元学习文本分类
Meta Learning for Few-Shot Medical Text Classification
论文作者
论文摘要
医疗专业人员经常在受限的数据设置中工作，以提供独特的人群中的见解。例如，一些医学观察会告知患者的诊断和治疗。这暗示了元学习的独特设置，这是一种快速学习新任务模型的方法，以提供其他方法无法实现的见解。我们研究了元学习和鲁棒性技术在广泛的基准文本和医学数据中的使用。为此，我们开发了新的数据管道，将语言模型与元学习方法结合在一起，并扩展了现有的元学习算法，以最大程度地减少最坏情况下的损失。我们发现，文本上的元学习是基于文本的数据的合适框架，它提供了更好的数据效率和与少数语言模型相当的性能，并且可以成功地应用于医疗注释数据。此外，结合DRO的元学习模型可以改善跨疾病代码的最坏情况损失。
Medical professionals frequently work in a data constrained setting to provide insights across a unique demographic. A few medical observations, for instance, informs the diagnosis and treatment of a patient. This suggests a unique setting for meta-learning, a method to learn models quickly on new tasks, to provide insights unattainable by other methods. We investigate the use of meta-learning and robustness techniques on a broad corpus of benchmark text and medical data. To do this, we developed new data pipelines, combined language models with meta-learning approaches, and extended existing meta-learning algorithms to minimize worst case loss. We find that meta-learning on text is a suitable framework for text-based data, providing better data efficiency and comparable performance to few-shot language models and can be successfully applied to medical note data. Furthermore, meta-learning models coupled with DRO can improve worst case loss across disease codes.
